The present invention relates to a speech coder for coding a speech signal with high quality at low bit rates, specifically, at about 8 to 4.8 kb/s.
As a method of coding a speech signal at a low bit rate of about 8 to 4.8 kb/s, CELP (Code Excited LPC Coding) is known, which is disclosed in, e.g., M. Schroeder and B. Atal, "Code-excited linear prediction: High Quality speech at very low bit rates", ICASSP, pp. 937-940, 1985 (reference 1). According to this method, on the transmission side, a spectrum parameter representing the spectrum characteristics of a speech signal is extracted from a speech signal of each frame (e.g., 20 ms). A frame is divided into subframes (e.g., 5 ms), and a pitch parameter representing a long-term correlation (pitch correlation) is extracted from a past sound source signal in units of subframes. Long-term prediction of speech signals in the subframes is performed using the pitch parameter to obtain difference signals. For the difference signal obtained by long-term prediction, one type of noise signal is selected so as to minimize the differential power between the speech signal and a signal synthesized by a signal selected from a code book constituted by predetermined types of noise signals. In addition, an optimal gain is calculated. Subsequently, an index representing the type of selected noise signal and the gain are transmitted together with the spectrum parameter and the pitch parameter. A description on the receiver side will be omitted.
As a method of quantizing a spectrum parameter, a scalar quantization method is used in reference 1. A vector quantization method is known as a method which allows more efficient quantization with a smaller amount of bits than the scalar quantization method. With regard to this method, refer to, e.g., Buzo et al., "Speech Coding Based upon Vector Quantization", IEEE Trans ASSP, pp. 562-574, 1980 (reference 2). In vector quantization, however, a data base (training data) for a learning procedure is required to form a vector quantization code book in advance. The characteristics of a vector quantizer depend on training data used. For this reason, the performance of the quantizer deteriorates with respect to a signal having characteristics which are not covered by the training data, resulting in a deterioration in speech quality. In order to solve such a problem, a vector/scalar quantization method is proposed, in which an error signal representing the difference between a vector-quantized signal and an input signal is scalar-quantized to combine the merits of the two methods. With regard to vector/scalar quantization, refer to, e.g., Moriya et al., "Adaptive Transform Coding of Speech Using Vector Quantization", Journal of the Institute of Electronics and Communication Engineers of Japan, vol. J. 67-A, pp. 974-981, 1984 (reference 3). A description of this method will be omitted.
In the conventional method disclosed in reference 1, in order to obtain high speech quality, the bit size of a code book constituted by noise signals must be set to be as large as 10 bits or more. Therefore, an enormous amount of operations are required to search the code book for an optimal noise signal (code word). In addition, since a code book is basically constituted by noise signals, speech reproduced by a code word selected from the code book inevitably includes perceptual noise.
Furthermore, in the conventional method in reference 1, since a spectrum parameter is quantized/coded by normal scalar quantization, a large number of bits are required for quantization. For this reason, it is difficult to decrease the bit rate while keeping high speech quality.
In the vector quantization method which is more efficient than the scalar quantization method, quantization characteristics depend on training data used for preparing a vector quantization code book. For this reason, the quantization performance deteriorates with respect to a signal having characteristics which are not covered by the training data, resulting in a deterioration in speech quality.
In the vector/scalar quantization method disclosed in reference 3, in addition to a code book table for vector quantization, another table is required to store information required for scalar quantization in accordance with the size of a code book for vector quantization. Assume that a 10th-order parameter and an 8-bit vector quantizer are used. The number of tables required for vector quantization is 256.times.10=2,560. The number of tables required for scalar quantization is 256.times.10=2,560. That is, a total of 5,120 tables are required, and hence a large memory capacity is required to store these tables.